Information about the spatial distribution of urban surface emissivity is essential for surface temperature estimation. The latter is critical in many applications, such as estimation of surface sensible and latent heat fluxes, energy budget, urban canopy modeling, bio-climatic studies and urban planning. This study proposes an estimation of urban surface emissivity, which is primarily based on spectral mixture analysis. The urban surface is assumed to consist of three fundamental land cover components, namely vegetation, impervious and soil that refer to the urban environment. Due to the complexity of the urban environment, the impervious component is further divided into two land cover components: high-albedo and low-albedo impervious. Emissivity values are assigned to each component based on emissivity distributions derived from the Landsat8. Following the proposed method, by combining the fraction of each cover component with a respective emissivity value, an overall emissivity for a given pixel is estimated. The methodology is applicable to visible and near infrared satellite imagery. Therefore it could be used to derive emissivity maps from most multispectral satellite sensors. The proposed approach was applied to Landsat8 multispectral data for the city of Darkhan-Uul, Mongolia. Emissivity, as well as land surface temperature maps in the spectral region of 10.6 - 11.2 μm (Landsat8 band 10) and 11.5-12.5 (Landsat8 band 11) were derived.